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Acceleration of ensemble machine learning methods using many-core devices

A. Tamerus, A. Washbrook, D. Wyeth
High Performance Computing Service, The University of Cambridge, Roger Needham Building, 7 JJ Thomson Avenue, Cambridge CB3 0RB, United Kingdom
Journal of Physics: Conference Series, 664 092026, 2015

@inproceedings{tamerus2015acceleration,

   title={Acceleration of ensemble machine learning methods using many-core devices},

   author={Tamerus, A and Washbrook, A and Wyeth, D},

   booktitle={Journal of Physics: Conference Series},

   volume={664},

   number={9},

   pages={092026},

   year={2015},

   organization={IOP Publishing}

}

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We present a case study into the acceleration of ensemble machine learning methods using many-core devices in collaboration with Toshiba Medical Visualisation Systems Europe (TMVSE). The adoption of GPUs to execute a key algorithm in the classification of medical image data was shown to significantly reduce overall processing time. Using a representative dataset and pre-trained decision trees as input we will demonstrate how the decision forest classification method can be mapped onto the GPU data processing model. It was found that a GPU-based version of the decision forest method resulted in over 138 times speed-up over a single-threaded CPU implementation with further improvements possible. The same GPU-based software was then directly applied to a suitably formed dataset to benefit supervised learning techniques applied in High Energy Physics (HEP) with similar improvements in performance.
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